Underwater gliders have been widely used in oceanography for a range of applications. However, unpredictable events like shark strikes or remora attachments can lead to abnormal glider behavior or even loss of the instrument. This paper employs an anomaly detection algorithm to assess operational conditions of underwater gliders in the real-world ocean environment. Prompt alerts are provided to glider pilots upon detecting any anomaly, so that they can take control of the glider to prevent further harm. The detection algorithm is applied to multiple datasets collected in real glider deployments led by the University of Georgia's Skidaway Institute of Oceanography (SkIO) and the University of South Florida (USF). In order to demonstrate the algorithm generality, the experimental evaluation is applied to four glider deployment datasets, each highlighting various anomalies happening in different scenes. Specifically, we utilize high resolution datasets only available post-recovery to perform detailed analysis of the anomaly and compare it with pilot logs. Additionally, we simulate the online detection based on the real-time subsets of data transmitted from the glider at the surfacing events. While the real-time data may not contain as much rich information as the post-recovery one, the online detection is of great importance as it allows glider pilots to monitor potential abnormal conditions in real time.

翻译：水下滑翔机已被广泛应用于海洋学的多种场景。然而，鲨鱼撞击或䲟鱼附着等不可预测事件可能导致滑翔机行为异常甚至设备丢失。本文采用异常检测算法评估真实海洋环境中水下滑翔机的运行状态。一旦检测到任何异常，系统会向滑翔机操控员发出即时警报，以便其及时介入控制滑翔机，避免进一步损害。该检测算法已应用于佐治亚大学Skidaway海洋研究所（SkIO）和南佛罗里达大学（USF）主导的实际滑翔机部署中采集的多个数据集。为验证算法通用性，实验评估针对四个滑翔机部署数据集展开，每个数据集均突出展示了不同场景下的各类异常。具体而言，我们利用回收后获取的高分辨率数据集对异常进行详细分析，并与操控员日志进行对比。此外，我们还基于滑翔机浮出水面时实时传输的数据子集模拟在线检测。尽管实时数据包含的信息丰富度不及回收后数据，但在线检测具有重大意义——它使滑翔机操控员能够实时监控潜在异常状况。